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Smart Grid Communication Networks


While there is a clear need for communication networks supporting reliable information transfer between the various entities in the electric grid, there are many issues related to network performance, suitability, interoperability, and security that need to be resolved. This project will focus on identifying opportunities to tailor communication protocols that have been designed for network traffic control to provide quality of service (QoS) to smart grid applications and to manage power flows and energy services in the smart grid between traditional and renewable generation sources and between utility-, third party-, and customer-owned assets. By creating collaborative links between the stakeholders, users, and standard developing organizations (SDOs) working on telecommunications, this project will promote the use and deployment of interoperable communication protocols for smart grid. In addition, the analytical and simulation tools and the published research findings to be produced by this project will foster the development of new areas of inquiry into smart grid specific communication technologies.


Objective: To accelerate the development of scalable, reliable, secure, and interoperable communications and standards for smart grid applications; and to enable informed decision making by smart grid operators by developing measurement science-based guidelines and tools.

What is the new technical idea?
Traditionally, technology decisions have been dictated by offerings of system vendors, while business decisions are regulated by federal, state, and regional regulatory commissions and organizations. While there are many choices of communications and networking standards, most of these standards were not developed specifically for smart grid applications or technologies. The new technical idea is to work directly with the smart grid stakeholders (utilities, regulators, service providers and consumers) and the telecommunication industry (vendors, SDOs, service providers) to identify communication requirements for smart grid applications, evaluate and develop communication standards, and develop guidelines and recommendations on their use and deployment. Also, the introduction of new power distribution technologies will transform electrical networks to more closely resemble the behaviors of communications networks. This creates an opportunity to apply well-established analysis and optimization techniques from the telecommunications field to aid in the design of future electrical networks.

What is the research plan?
Our research plan is focused on understanding and modeling the power grid user and system behaviors and developing control and communication strategies for achieving the smart grid vision of a more efficient and dynamic electric grid.

Our previous work evaluated machine learning techniques to support the creation of enhanced wireless networks that will enable next-generation Cyber-Physical Systems (CPS), the Internet of Things (IoT), and the Smart Grid, which will incorporate both CPS and IoT architectural elements. In FY 2020, we will develop and apply performance measurement tools and metrics to evaluate machine learning architectures/models and algorithms in communication networks for Industrial IoT (I-IoT) systems, including the Smart Grid. We will examine how control and networking system co-design can improve I-IoT performance, and we will investigate how machine learning techniques can be used to make I-IoT systems more resilient.  We will also explore how machine learning techniques can be used to detect abnormal system activity, whether due to errors or malicious actors. Our work will consider the accuracy of machine learning architectures and algorithms, and we will evaluate the computational overhead associated with these algorithms. In FY 2020, we will also study resource allocation schemes that can meet the diversified QoS requirements of machine-type traffic, which is different from traditional Internet traffic, and we will develop an understanding of telemetry data for supervised learning algorithms. We will use this understanding to design low-cost supervised on-line learning algorithms to support dynamic resource allocation for I-IoT networks.

With the proliferation of Ethernet-based networks and widening PMU installations in massive numbers, future PMU characteristics will require synchronous data delivery with constant latency and minimum jitter. In addition, keeping up with the ever-increasing integration of multiple sub-networks to cope with the massive number of PMU devices, as well as other sensor and actuators, will require the support of a reliable high-speed network such as TT-Ethernet. TT-Ethernet is a deterministic, synchronized, and congestion-free network protocol, which is based on the IEEE 802.3 Ethernet. It is designed to fulfill the requirements of reliable, real-time data delivery in advanced integrated systems.  Our prime objective in FY 20 and beyond is to develop TT-Ethernet for synchrophasor communications. We will specifically aim at developing a wire-based synchrophasor network that is capable of communications between PMUs and PDCs via virtual links. The immediate challenge is to interface each PMU device to the TT-E end-system, as well as develop virtual links that can support communication between PMUs and their PDC. In addition, to assess the performance of a large PMU network, a TT-E simulation-based system will be investigated using the EMTP software tool. Such a system can be used to develop a hardware-in-the loop testbed capable of deploying Virtual PMUs.

 Finally, in FY ‘19 we completed our software implementation of a new two-stage processing approach that consists of a subspace-based estimation method to detect and identify all harmonic components, followed by a low-complexity fast tracking algorithm to monitor frequency variations of voltage and current signals in real-time with great accuracy. The outcome of this investigation has been published in the May, 2019 issue of the IEEE Transactions on Smart Grid. In this investigation we also studied PMU-based modal identification methods (such as recursive Fourier transform) to explore the possibility of expanding the application of our harmonic tracking approach for identifying power damping and oscillation disturbances for wide area monitoring.

Created December 3, 2012, Updated October 17, 2019